This course teaches two fundamental math topics: linear algebra and probability theory. The course is divided equally into two parts. The first part is for linear algebra, including systems of equations, vector spaces, determinants, eigenvalues, linear transformation and their applications. The second part covers topics in probability theory related to basic notions of probability, conditional probability and inference, random variables and respective distribution functions, expectations (mean and variance), covariance and correlation, the entropy of, and between random variables is presented and its application to Markov chains.

Target audience

The course addresses to students of medicine, biology, computer science, chemistry and mathematics, who wish to understand basic and advanced concepts of linear algebra and probability theory.

Basic Prerequisites

Basic knowledge of mathematics. Knowledge of programming, in R/Matlab would be beneficial.

Course Goals

·Understanding and solving linear algebra and related applications.

·Understanding and interpreting of fundamental concepts of probability theory.

The course covers all fundamental concepts, topics and techniques of Molecular and Cellular Biology from the chemical basis of life, to the central dogma of Molecular Biology, and the cellular organization of biological systems, presents the functional specialization of cell and tissues, and concludes with the molecular events underlying complex traits and diseases. Throughout this course the advances of analytical technologies and challenges of quantitative and computational approaches in Biomedical Sciences and research are highlighted.

Basic Prerequisites

None

Course Goals

The course is designed specifically for graduate students who didn’t have ANY biological education in their previous studies and in particular for Computer and Physical scientists, Mathematicians and Statisticians. It is a fast and intense means to cover the basic biological concepts and technologies and provide with the adequate background knowledge of Molecular and Cellular Biology to understand and address the cutting-edge challenges and questions of Biomedical and Life Science research in general.

Experimental and computational approaches in Biology: Model animal systems and population approaches

12

Drug discovery processes, Biotechnological applications

13

Exercise: Paper presentationand/or Seat-in Exam

Course Name: Introduction to Genetics and Evolutionary Biology

Course Code: COMP-102

Semester: Fall

Coordinator: Yiannis Iliopoulos

Instructors: Yiannis Iliopoulos, Pantelis Topalis, Tereza Manousaki

Summary

This course discusses the principles of genetics and evolution with application to the study of biological function at the level of molecules, cells, and multicellular organisms, including humans. The topics include: structure and function of genes, chromosomes and genomes, biological variation resulting from recombination, mutation, and selection, population genetics, use of genetic methods to analyze protein function, gene regulation and inherited disease. Students are required to attend a exercise/problem solving session (joint class with the sophomores of the department of Biology).

Basic Prerequisites

None

Course Goals

To provide basic knowledge in genetics and evolution.

Being able to understand the biological function at the level of molecules, cells and multicellular organisms.

The course covers basic areas of statistics, such as graphical representations of data, random variables, types of sampling and design, estimation via maximum likelihood. Hypothesis testing and confidence intervals (for means and proportions), type I and II errors and p-values. Hypothesis testing via computational techniques (bootstrap and permutation). On a second phase, correlations for continuous variables (Pearson and Spearman coefficients), association of categorical variables (G2 test of independence) and linear regression. Finally, false discovery methods in multiple hypothesis testing will be mentioned. Demonstration and exercises using the R statistical package will be considered as well.

Target audience

The course addresses to students of medicine, biology, computer science, chemistry and mathematics, who have a minor knowledge of statistics and wish to understand in more depth certain statistical terms.

Prerequisites

A basic knowledge of mathematics and statistics. Knowledge of programming, in R would be beneficial.

Hypothesis testing: one and two means with and without computational techniques, relationship with confidence intervals, explanation of concepts like type I and II errors and p-values + demonstration with R.

This course aims to describe some of the most popular biomedical databases covering a wide range of different datatypes. It will present the different ways the same information is often stored in different databases and the problems that are caused by those. Proper use of controlled vocabularies and ontologies can provide a solution and can promote data integration.

Basic Prerequisites

A preparatory course on molecular biology. Basic knowledge of a scripting language it would be useful.

Course Goals

Becoming familiarized with the various kinds of biological datatypes and formats..

Being able to extract relevant information for a project from different sources.

Learn how to massively access the data stored in databases via their API.

The course will introduce the R statistical software as a tool for performing data analysis tasks in the bioinformatics field. At the beginning, the basics of the R language will be explained, along with the main concepts related to the R software and its modular architecture. Most advanced concepts will then be introduced, as for example data structure in R, functional programming, graphical visualization and the creation of R packages. The second part of the course will focus on the Bioconductor initiative and its repository of R packages for bioinformatics. Particularly, functionalities for analyzing RNA-seq and microarray data will be explored in detail.

Basic Prerequisites

Elementary knowledge of programming and statistics.

Course Goals

At the end of the course, the students are supposed to:

.know the capabilities of the R software and its possible uses;

.master the R language and being able to use it for writing scripts and simple data analysis pipelines

.know the scope and characteristics of the Bioconductor initiative

.be able to identify and use the most suitable Bioconductor packages for a given data analysis task.

Recapitulation of the previous lessons and assignment of the final project

Course Name: Methods in Bioinformatics

Course Code: BC204

Semester: Spring

Coordinators: Panayiota Poirazi, Ioannis Tsamardinos

Instructors: Panayiota Poirazi, Ioannis Tsamardinos

Summary

This course aims to describe some of the most prominent and most widely used methods for the analysis of biological data, with emphasis on different large-scale data sets (e.g. microarray gene expression data, RNAseq data, metagenomics, biological networks etc). The course focuses on different methodologies for dimensionality reduction, feature selection, model selection, clustering, classification and network inference. The main goal of the course is not to describe in detail the most sophisticated implementations but to present the features and rational behind each method and its appropriatness for solving specific problems. Classes will include theoretical lectures as well as practical exercises, where students will be required to utilize existing software tools containing the presented methods to solve selected problems.

Basic Prerequisites

A preparatory course on molecular biology. Basic knowledge of math and statistics.

Course Goals

.Becoming familiarized with the most common problems in the analysis of different types of large-scale biological data.

This course aims to describe some of the most prominent and most widely used algorithms for the analysis of biological data, with emphasis on handling and analyzing biological sequences. The course focuses on a detailed description of algorithms for alignment, the rapid search of short sequences, tracing patterns and finding motifs in sequences, sequence assembly and phylogenetic analysis among others. The main goal of the course is not to describe in detail the most sophisticated implementations but to present the rationale behind the design of algorithms in a constructive and educational manner from both theoretical and practical viewpoints. Classes will include theoretical lectures as well as practical exercises, where students will be required to implement algorithms in the language of their choice.

Basic Prerequisites

A preparatory course on molecular biology. Basic knowledge of math and statistics.

Course Goals

.Becoming familiarized with the most common problems in the analysis of biological sequences.

.Being able to categorize the algorithms that find wide use in bioinformatics (dynamic programming, randomized algorithms, divide and conquer algorithms etc).

.Achieving a high level of competence in the performance of alignment and BLAST searches.

.Acquiring the ability to implement simple algorithms from the blackboard to the keyboard.